texture.triangle.low.rv.high: Soil Texture Low-RV-High as Defined by Quantiles

Description

This function accepts soil texture components (sand, silt, and clay percentages) and plots a soil texture triangle with a "representative value" (point) and low-high region (polygon) defined by quantiles. Marginal quantiles of sand, silt, and clay are used to define the boundary of a low-high region that encloses a severall likely soil texture classes based on the values in ssc. The defualt settings place the RV symbol at the texture defined by marginal medians of sand, silt, and clay. The default low-high region is defined by the 5th and 95th marginal percentiles of sand, silt, and clay.

Usage

texture.triangle.low.rv.high(ssc, p=c(0.05, 0.5, 0.95), delta=1, 
pop.rv.col='red', range.col='RoyalBlue', range.alpha=75, 
sim=FALSE, sim.n=1000, sim.rv.col='yellow', sim.col=grey(0.95), 
sim.alpha=150, legend.cex=0.75, ...)

Arguments

ssc

a matrix-like object with columns: 'sand', 'silt', 'clay', values are percentages that should add to 100.

percentiles defining 'low', 'representative value', and 'high'

delta

step-size used to form low-high region

pop.rv.col

the symbol color used to denote the population representative value on the texture triangle

range.col

color of the polygon enclosing the low-high region

range.alpha

transparency of the low-high range polygon (0-255)

sim

optional simulation of low-rv-high values based on a composition drawn from normal distributions, this requires the `compositions` package

sim.n

number of simulated sand, silt, and clay values

sim.rv.col

the symbol color used to denote the simulated representative value on the texture triangle

sim.col

color of the simulated low-high range polygon

sim.alpha

transparency of the simulated low-high range polygon (0-255)

legend.cex

scaling factor for legend

...

further arguments passed to triax.points

Value

A high-level plot as generated by soil.texture.

Details

Simulated sand, silt, and clay values are based on sampling from a normal distribution as performed by rnorm.acomp in the `comppositions` package. The mean vector of the sand, silt, and clay values, along with covariance matrix derived from ssc are used to parametrize sampling.

Examples

Run this code

# sample data
data(loafercreek, package='soilDB')

# extract sand, silt, clay proportions
x <- na.omit(data.frame(sand=loafercreek$sand, silt=loafercreek$silt, clay=loafercreek$clay))

# test out the function
texture.triangle.low.rv.high(x, p=c(0.05, 0.5, 0.95))
texture.triangle.low.rv.high(x, p=c(0.25, 0.5, 0.75), range.col='darkgreen')

# simulate compositional data from source data
if(require(compositions)) {
  # add simulated low-rv-high
  texture.triangle.low.rv.high(x, p=c(0.05, 0.5, 0.95), sim=TRUE)
}